Content distribution network supporting popularity-based caching

ABSTRACT

A content delivery network may provide content items to requesting devices using a popularity-based distribution hierarchy. A central analysis system may determine popularity data for a content item stored in a first caching device. The central analysis system may determine that a change in the popularity data is beyond a threshold value. The central analysis system may then transmit an instruction to move the content item from the first caching device to a second caching device in a different tier of caching devices than the first caching device. The central analysis system may update a content index to indicate that the content item has been moved to the second caching device. A user device may be redirected to request the content item directly from the second caching device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S.application Ser. No. 17/086,529, filed Nov. 2, 2020, which is acontinuation of U.S. application Ser. No. 16/429,902, filed Jun. 3, 2019and now U.S. Pat. No. 10,848,587, which is a continuation of U.S.application Ser. No. 14/857,281, filed Sep. 17, 2015 and now U.S. Pat.No. 10,356,202, which is a continuation of U.S. application Ser. No.13/590,746, filed Aug. 21, 2012 and now U.S. Pat. No. 9,167,049, whichclaims priority to Provisional Application No. 61/594,017, filed Feb. 2,2012, each of which are incorporated herein by reference in theirentirety.

BACKGROUND

Content delivery networks (CDNs) enable provision of content, such asaudio and video content, to users. The CDNs typically employ amulti-tiered hierarchy of computer servers, with edge cache servers thatserve requests for content from client devices, intermediate or mid-tiercache servers that serve requests from edge cache servers and othermid-tier cache servers, and an origin server that originally stores andsupplies the content. The purpose of this hierarchy is to create ascaleout network that can serve a large number of user requests.However, if only one origin server serves each user request, that serverwould quickly be overloaded if the content it served grew in popularity.

In a typical system, every user request goes through an edge cacheserver, regardless of the popularity of the content being requested.When the content library being served by the network is very large orhas a “long tail,” it may become difficult to efficiently manage theedge cache servers. One conventional solution is to use page replacementalgorithms, such as Least Recently Used (LRU) or LRU-K, to purge contentfrom a cache server when it has not been recently requested.

When using a page replacement algorithm to purge unpopular content, thecontent is still served and cached by an edge cache server and anymid-tier cache servers. For example, if an origin server hosts a contentlibrary of 1,000 videos, and one of the videos is requested by only oneuser, the user request is served by the origin server, any mid-tiercache servers, and the edge cache server. If LRU is being used, then notonly is the request served by multiple cache servers, but the content isalso cached by the cache servers that request it. If a cache server isfull, then caching new content requires the purge of already-cachedcontent. No consideration is given to the fact that the purged contentmay actually be more popular than the newly-cached content. With asufficiently large content library this may lead to severe cachethrashing. A cache is considered to be thrashing when it is constantlypurging and filling content, as opposed to serving from the cache. Aseverely thrashing cache may put as much load on the network as routingall requests to the origin. It may be very difficult to size an edgecache server appropriately for a large and growing content library.

Accordingly, there remains a need to improve the delivery of content,and to balance that need with the strains on the network.

SUMMARY

Some features described herein relate generally to a hierarchy ofstorage and processing devices, such as a CDN, that implements apopularity-based distribution hierarchy where content is cached andserved based on its popularity. Content need not be replicated acrossthe different vertical tiers of the CDN but may be stored in aparticular tier (e.g., a popularity tier) based on its popularity. Insome instances, this may significantly decrease the storage requiredbecause each content item is only stored once, according to itspopularity, as opposed to being stored in multiple places. As a result,a request for content may be routed directly to any individual tier ofthe CDN where content matching the request is located and need notrequire that content go through an edge server of the CDN.

In an embodiment, a computing device may determine popularity data for acontent item stored in a caching device, such as a cache server. Thecontent item may be, for example, a media content item, such as a video,available for on-demand access by a user device, such as a set top box,television, computer, or mobile phone. The popularity data may be, forexample, a popularity ranking value determined by comparing a popularityvalue of the content item to the respective popularity values of othercontent items in the CDN's content library. For example, the computingdevice may determine that the most popular content item in the contentlibrary has a popularity ranking value of 1, while the 29th most popularcontent item in the content library has a popularity ranking value of29. The popularity values may be based on, for example, the number ofreceived requests (or transmitted streams) for the content item, inwhich more recent requests or streams are weighted more heavily thanolder requests or streams.

At a later time, the computing device may determine that a change in thepopularity data of the content item has occurred. For example, thecontent item's popularity ranking value may have increased or decreasedover time. The computing device may compare the change in the popularitydata to a threshold value, such as a popularity ranking threshold value.The threshold value may be a dynamic threshold value based on the numberand size of the content items in the content library and the number andsize of the caching devices in the popularity-based distributionhierarchy. If the change in popularity is beyond the threshold value,the computing device may move the content item to another caching devicein a different tier of the popularity-based distribution hierarchy.

For example, in response to an increase in popularity beyond thethreshold value, the computing device may transmit an instruction tomove the content item from a cool caching device, which may beimplemented using the origin server, to a hot caching device, which maybe implemented using an edge caching device. In another example, inresponse to a decrease in popularity beyond the threshold value, thecomputing device may transmit an instruction to move the content itemfrom a hot caching device to a medium caching device. The computingdevice may then update a content index to update the locationinformation for obtaining the content item. Subsequently, when a requestis received from a user device for the content item, the user device maybe redirected to request the content item directly from the cachingdevice in the different tier to which the content item has been moved.

This summary is not intended to identify critical or essential featuresof the disclosures herein, but instead merely summarizes certainfeatures and variations thereof. Other details and features will also bedescribed in the sections that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

Some features herein are illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements.

FIG. 1 illustrates an example network environment.

FIG. 2 illustrates an example software and hardware device on whichvarious elements described herein can be implemented.

FIGS. 3A-3B illustrate an example content delivery network.

FIGS. 4A-4C illustrate another example content delivery network.

FIGS. 5A-5B illustrate an example technique for providing a contentitem.

FIGS. 6A-6B illustrate another example technique for providing a contentitem.

FIG. 7 illustrates an example technique for determining a dynamicthreshold value.

FIG. 8 illustrates an example process flow for providing a content item.

DETAILED DESCRIPTION

FIG. 1 illustrates an example information distribution network 100 inwhich many of the various features described herein may be implemented.The illustrated information distribution network is only one example ofa suitable network and is not intended to suggest any limitation as tothe scope of use or functionality of the disclosure. The illustratednetwork should not be interpreted as having any dependency orrequirement relating to any component or combination of components in aninformation distribution.

Network 100 may be a telecommunications network, a multi-serviceoperator (MSO) network, a cable television (CATV) network, a cellularnetwork, a wireless network, an optical fiber network, a coaxial cablenetwork, a hybrid fiber-coaxial (HFC) network, or any other suitabletype of information distribution network or combination of networks. Forexample, network 100 may be a cellular broadband network communicatingwith multiple communications access points, such as wirelesscommunications tower 130. In another example, network 100 may be acoaxial system comprising a cable modem termination system (CMTS)communicating with numerous gateway interface devices (e.g., gatewayinterface device 111 in example home 102 a). In another example, thenetwork 100 may be a fiber-optic service system comprising opticalfibers extending from an optical line terminal (OLT) to numerous opticalnetwork terminals (ONTs) communicatively coupled with various gatewayinterface devices. In another example, the network 100 may be a digitalsubscriber line (DSL) system that includes local office 103communicating with numerous gateway interface devices. In anotherexample, network 100 may be an HFC network in which Internet traffic isrouted over both optical and coaxial communication paths to a gatewayinterface device in or near a user's home. Various aspects of thedisclosure may operate on one or more of the networks described hereinor any other suitable network architectures now known or laterdeveloped.

Network 100 may use a series of interconnected communication links 101(e.g., coaxial cables, optical fibers, wireless links, etc.) to connectpremises such as homes 102 or other user environments to local office103. Communication links 101 may include any suitable wiredcommunication links, wireless communication links, communicationsnetworks, or combinations thereof. For example, portions ofcommunication links 101 may be implemented with fiber-optic cable, whileother portions of communication links 101 may be implemented withcoaxial cable. Communication links 101 may also include variouscommunications components such as splitters, filters, amplifiers,wireless components, and other suitable components for communicatingdata. Data may include, for example, internet data, voice data, weatherdata, content data, and any other suitable information. Content data mayinclude, for example, video content, audio content, media on demand,video on demand, streaming video, television programs, text listings,graphics, advertisements, and other content. A content item mayrepresent an individual piece of content, such as a media content item(e.g., a particular movie, television episode, online video clip, song,audio recording, image, or other media content) or any other data. Insome instances, a content item may be fragmented into segments, such asa plurality of two-second video fragments that may be separatelyaddressed and retrieved.

Local office 103 may transmit downstream information signals ontocommunication links 101, and premises such as home 102 may receive andprocess those signals. In certain implementations, communication links101 may originate from local office 103 as a single communications path,and may be split into any suitable number of communication links todistribute data to homes 102 and various other destinations. Althoughthe term home is used by way of example, homes 102 may include any typeof user environment, such as single family homes, apartment complexes,businesses, schools, hospitals, parks, and other suitable environmentsand combinations of environments.

Local office 103 may include interface 104, which may be a computingdevice configured to manage communications between devices on thenetwork of communication links 101 and backend devices, such as server105, server 106, and server 107. For example, interface 104 may be acable modem termination system (CMTS). The termination system may be asspecified in a standard, such as, in an example of an HFC-type network,the Data Over Cable Service Interface Specification (DOCSIS) standard,published by Cable Television Laboratories, Inc. The termination systemmay be configured to transmit data over one or more downstream channelsor frequencies to be received by various devices, such as modems inhomes 102, and to receive upstream communications from those modems onone or more upstream frequencies.

Local office 103 may include one or more network interfaces 108 forcommunicating with one or more external networks 109. One or moreexternal networks 109 may include, for example, one or moretelecommunications networks, Internet Protocol networks, cellularcommunications networks (e.g., Global System for Mobile Communications(GSM), Code Division Multiple Access (CDMA), and any other suitable 2nd,3rd, 4th and higher generation cellular communications networks),cellular broadband networks, radio access networks, fiber-opticnetworks, local wireless networks (e.g., Wi-Fi, WiMAX), satellitenetworks, and any other suitable networks or combinations of networks.

Local office 103 may include a variety of servers that may be configuredto perform various functions. Local office 103 may include one or morepush servers 105 for generating push notifications to deliver data,instructions, or both to devices that are configured to detect suchnotifications. For example, push server 105 may transmit an instructionto a device to transfer service from one wireless network orcommunications access point to another wireless network orcommunications access point. Local office 103 may include one or morecontent servers 106 configured to provide content (e.g., media content)to devices. Local office 103 may include one or more application servers107. For example, application server 107 may be used to implement acaching device, such as a cache server, for the content stored in orprovided by content server 106.

Homes 102 may include a single family home, an apartment, an outdoorrestaurant, an office suite, or any other suitable indoor environmentand extend to an outdoor environment. Example home 102 a may include aninterface 120, which may include device 110, for communicating oncommunication links 101 with local office 103, one or more externalnetworks 109, or both. For example, device 110 may be a coaxial cablemodem (for coaxial cable links 101), a broadband modem (for DSL links101), a fiber interface node (for fiber-optic links 101), or any othersuitable device or combination of devices. In certain implementations,device 110 may be a part of, or communicatively coupled to, gatewayinterface device 111. Gateway 111 may be, for example, a wirelessrouter, a set-top box, a computer server, or any other suitablecomputing device or combination.

Gateway interface device 111 may be any suitable computing device forcommunicating with device 110 to allow one or more other devices inexample home 102 a to communicate with local office 103, one or moreexternal networks 109, or other devices communicatively coupled thereto.Gateway 111 may include local network interfaces to providecommunication signals to user devices in or near example home 102 a,such as television 112, set-top box 113, personal computer 114, laptopcomputer 115, wireless device 116 (e.g., a wireless laptop, a tabletcomputer, a mobile phone, a portable gaming device), vehicular computingsystem 117 (e.g., a mobile computing system, navigation system, orentertainment system in an automobile, marine vessel, or aircraft) andany other suitable device.

FIG. 2 illustrates general hardware elements and software elements thatcan be used to implement any of the various computing devices and/orsoftware discussed herein. Device 200 may include one or more processors201, which may execute instructions of a computer program to perform anyof the functions and steps described herein. The instructions may bestored in any type of computer-readable medium or memory to configurethe operation of the processor 201. For example, instructions may bestored in a read-only memory (ROM) 202, random access memory (RAM) 203,removable media 204, such as a Universal Serial Bus (USB) drive, compactdisk (CD) or digital versatile disk (DVD), hard drive, floppy diskdrive, or any other desired electronic storage medium. Instructions mayalso be stored in hard drive 205, which may be an internal or externalhard drive.

Device 200 may include one or more output devices, such as a display206, such as an external monitor or television, and may include one ormore output device controllers 207, such as a video processor. In someembodiments, device 200 may include one or more user input devices 208,such as a remote control, keyboard, mouse, touch screen, microphone, orany other suitable input device.

Device 200 may also include one or more network interfaces, such asnetwork input/output (I/O) interface 210 to communicate with an externalnetwork 209. The network interface may be a wired interface, wirelessinterface, or a combination of the two. In some embodiments, network I/Ointerface 210 may include a cable modem, and network 209 may include thecommunication links 101 shown in FIG. 1 , one or more external networks109, an in-home network, a provider's wireless, coaxial, fiber, orhybrid fiber/coaxial distribution system (e.g., a DOCSIS network), orany other desired network.

FIGS. 3A-3B illustrates an example distribution hierarchy for a contentdelivery network (CDN). After a content item is requested by a clientdevice, such as one of client devices 303 a-d, it is replicated from theorigin of the CDN, such as one or more origin servers 300 in tier 310,downward through tiers of lower-tier cache servers, such as mid-tiercache servers 301 a-b in tier 320 and edge cache servers 302 a-c in tier330. In some arrangements, mid-tier cache servers 301 may includemultiple tiers or layers of cache servers (e.g., in tiers different fromtier 320). The various components shown in FIGS. 3A-3B may beimplemented using any suitable hardware, software, or both, such asdevice 200 shown in FIG. 2 .

One or more origin servers 300 may store the original copy of a contentitem, such as a movie or other data available for on-demand access byclient devices 303 a-d. Origin server 300 may also establish thedistribution hierarchy used to cache and distribute its content. Forexample, origin server 300 may transmit messages to one or more cacheservers requesting that they cache origin server 300's content, andinstructing each cache server as to where it should be positioned in thedistribution hierarchy. Although FIG. 3A illustrates a single originserver 300 for a given distribution hierarchy, the CDN may includemultiple origin servers located in the same or different tiers as originserver 300. In some instances, each individual content item may have itsown distribution hierarchy. As a result, a single server may act as bothan origin server for one content item and a mid-tier cache server foranother content item.

To facilitate distribution of the content, one or more mid-tier cacheservers 301 a-b and edge cache servers 302 a-c may be communicativelycoupled to origin server 300, and each server may store a copy of thefile(s) containing the content of the origin server 300, such as a copyof the movie files for an on-demand movie. Edge cache servers 302 a-cmay serve requests for content items from client devices 303 a-d andrely on higher lever servers, such as mid-tier cache servers 301 a-b andorigin server 300, to supply source files for the content and toestablish access authorization parameters for the content. Mid-tiercache servers 301 a-b and origin server 300 may refrain from interactionwith client devices 303 a-d, and may instead limit their communicationsto serving requests from edge cache servers 302 a-c or other servers inthe CDN.

The CDN may also include one or more content routers 304 forfacilitating communications between the servers of the distributionhierarchy. Content router 304 may be communicatively coupled to all ofthe cache servers in the CDN, but need not be a part of the distributionhierarchy. For example, content router 304 may receive a request for acontent item from one of client devices 303 a-d and redirect the clientdevice to request the content item from one of edge cache servers 302a-c. Content router 304 may select the edge cache server based on theability of the edge cache server to serve the requested content item,the proximity between the client device and the edge cache server, andthe relative load of the edge cache server. Proximity may be, forexample, network proximity, geographic proximity, or a combination ofthe two. For example, the proximity between client devices 303 a-d andedge cache servers 302 a-c in tier 330 may be closer than the proximitybetween client devices 303 a-d and origin server 300 in tier 310.

As illustrated in FIG. 3B, the hierarchy of caching servers may be usedto assist with the distribution of media content by servicing requestsfor that content on behalf of the content's source. For example, clientdevice 303 may initially request content item 350 from edge cache server302. Edge cache server 302 may determine that it does not possesscontent item 350 and request content item 350 from mid-tier cache server301. Mid-tier cache server 301 may also determine that it does notpossess content item 350 and request it from origin server 300. Originserver 300, which serves as the source for content item 350, may receivethe request and transmit content item 350 to mid-tier cache server 301,which may in turn transmit content item 350 to edge cache server 302.Edge cache server 302 may then transmit content item 350 to clientdevice 303.

In the CDN of FIGS. 3A-3B, every request for content is routed to anedge cache server regardless of the popularity of the content beingrequested. Since each request is served by one or more origin servers300, one or more origin servers 300 may quickly become overloaded ifcontent item 350 increases in popularity. Further, when the contentlibrary being served by the CDN is very large or has a “long tail” ofmid-tier cache servers, it may become difficult to efficiently size theedge cache servers 302 a-c. Thus, improved systems, apparatuses, andmethods for providing content are discussed with reference to FIGS. 4-8.

FIGS. 4A-4C illustrate an exemplary popularity-based distributionhierarchy for a CDN where a content item, such as a media content item,requested by a device, such as a user device, is transmitted to thedevice directly from its source without replication across intermediatetiers of the distribution hierarchy. The source location may be selectedas a function of the popularity of the content item. The variouscomponents and processes described with reference to FIGS. 4A-4C may beimplemented using any suitable hardware, software, or both, such asdevice 200 shown in FIG. 2 .

One feature of the popularity-based distribution hierarchy of FIGS.4A-4C is that content providers may increase the size of the contentlibrary available to their users. The disclosed features allow contentproviders to offer more content to users without requiring significantnew hardware deployment by moving from a CDN model based on replicationof content items across vertical tiers of cache servers, such as the CDNof FIGS. 3A-3B, to a model where subsets of content items are cached andserved from particular popularity tiers of caching devices, such ascache servers, based on popularity, such as the CDN of FIGS. 4A-4C. Inan illustrative example, cool caching device 400 in tier 410 may beimplemented using origin server 300 in tier 310, medium caching devices401 a-b in tier 420 may be implemented using mid-tier cache servers 301a-b in tier 320, and hot caching devices 402 a-c in tier 430 may beimplemented using edge cache servers 302 a-c in tier 330. This maysignificantly decrease the storage required because each content itemmay only be stored once in a popularity tier, according to itspopularity, as opposed to being stored in multiple places. This may alsoeffectively partition the CDN's content library across a logicaldistribution hierarchy, allowing each tier of the popularity-baseddistribution hierarchy to function on a subset of the total contentitems.

As illustrated in FIG. 4A, the popularity-based distribution hierarchymay include hot caching devices 402 a-c in tier 430, medium cachingdevices 401 a-b in tier 420, and cool caching device 400 in tier 410.Popular content items may be cached and served by hot caching devices402 a-c. Modestly popular content items may be cached and served bymedium caching devices 401 a-b. Unpopular content items, such as “longtail” content items, may be cached and served directly by cool cachingdevice 400 or an origin server. The proximity (e.g., network proximity,geographic proximity, or a combination of the two) between devices 403a-d (e.g., user devices, client devices) and hot caching devices 402 a-cmay be closer than the proximity between devices 403 a-d and mediumcaching devices 401 a-b, and the proximity between devices 403 a-d andmedium caching devices 401 a-b may be closer than the proximity betweendevices 403 a-d and cool caching device 400.

In some embodiments, the CDN does not replicate a content item acrosscaching devices in different popularity tiers, such as tiers 410, 420,and 430, of the popularity-based distribution hierarchy but mayreplicate the content item across caching devices in the same tier. Forexample, hot caching devices 402 a-c may store copies of a popularcontent item, medium caching devices 401 a-b may cache and serve copiesof a modestly popular content item, and cool caching device 400 maycache and serve copies of an unpopular content item. A content itemcached in hot caching devices 402 a-c in tier 430 may not be replicatedto medium caching devices 401 a-b in tier 420 or to cool caching device400 in tier 410. The content item may be replicated to other hot cachingdevices in tier 430.

The CDN may include content router 404 for receiving requests forcontent items from devices 403 a-d and for redirecting devices to thetier of hot, medium, or cool caching devices where the content item isstored. Content router 404 may be communicatively coupled to all of thecaching devices in the CDN, but need not be a part of thepopularity-based distribution hierarchy or located in any particulartier. For example, the content router may be located anywhere in the CDNand may not serve content. In another example, content router 404 may beimplemented as a logical server using a portion of the hardware used toimplement a different server, such as cool caching device 400.

To facilitate the popularity-based distribution hierarchy, contentrouter 404 may store content index 407. Content index 407 may be or haveaccess to a database that includes, for example, location informationfor obtaining each of the content items in the CDN's content library.When a content item is moved to a different tier of caching devices,content index 407 may be updated with new location information forobtaining the content item.

Content router 404 may redirect a device to request a content item froma particular caching device or tier of caching devices using anysuitable technique, such as by transmitting a Hypertext TransferProtocol (HTTP) 302 redirect instruction. For example, content router404 may receive a request for a content item from device 403 a, accesscontent index 407 to determine where the content item should be servedfrom, and transmit an HTTP 302 redirect instruction to device 403 a toredirect it to one of hot caching devices 402 a-c in tier 430, mediumcaching devices 401 a-b in tier 420, or cool caching device 400 in tier410 based on where the content item should be served from. In anotherexample, content router 404 or caching devices 400, 401 a-b, 402 a-c maydynamically redirect a device to a different tier of caching devices ifthe popularity of a content item changes while it is being downloaded bythe device. Dynamic redirection of a request to a caching device in adifferent tier will be discussed further with reference to FIGS. 5A-5Band FIGS. 6A-6B.

The CDN may include central analysis system 406 for determining therespective popularities of the content items in the CDN's contentlibrary and for moving one or more of the content items to a hot,medium, or cool caching device based on their respective popularities.The popularity of a content item may correspond to a popularity value, apopularity ranking value, or any other suitable value indicative of thecontent item's popularity. Central analysis system 406 may becommunicatively coupled to content router 404, but need not be a part ofthe popularity-based distribution hierarchy or located in any particulartier.

Central analysis system 406 may store or have access to request history405 for use in determining popularity information, such as popularitydata 408. Request history 405 may comprise a database that includesinformation received from content router 404, such as, for example, ahistory of all requests received for each of the content items in theCDN's content library, a history of all streams of the content itemstransmitted to devices 403 a-d, or any other suitable information. Forexample, request history 405 may include, for each content item, one ormore content item identifiers, such as a uniform resource identifier(URI), in association with the respective dates and times that thecontent item was requested, streamed, or both. The content itemidentifier may be, for example, a uniform resource identifier (URI)comprising a string of characters used to identify the content item,such as the URI “http://videocontent.net/movies/Avatar/140880/movie-1”corresponding to the motion picture Avatar or a unique fragment of themotion picture Avatar. Popularity data 408 may include, for example,popularity values and popularity ranking values for each content item inthe CDN's content library. For example, popularity data 408 may store,for each content item, a content item identifier in association withpopularity values, popularity rankings, and the respective dates andtimes that the each of the popularity values, popularity rankings, orboth were determined.

Central analysis system 406 may determine a respective popularity valuefor each content item in the CDN's content library using any suitabletechnique. For example, central analysis system 406 may determine apopularity value p for a content item based on its request history asshown in Equation 1:

$\begin{matrix}{p = \frac{N}{t}} & (1)\end{matrix}$where N represents the number of requests for the content item in agiven period of time t. In one variation, N may represent the number ofstreams of the content item transmitted from the CDN's caching devicesto various devices in a given period of time t.

In another embodiment, central analysis system 406 may determine apopularity value for a content item by weighting more recent popularityover older popularity, such as by implementing a reduction factoragainst older requests. For example, central analysis system 406 maydetermine a popularity value p for a particular content item based onthe number of requests (or streams) for the content item as a functionof time as shown in Equation 2:

$\begin{matrix}{p = {\sum\limits_{i = {f(j)}}\frac{N_{j}}{j}}} & (2)\end{matrix}$where j is an integer value, j represents j-th time duration (e.g., thelast minute, the last hour, one to two hours ago, etc.), and N_(j)represents the number of requests (or streams) for the content itemduring the j-th time duration. For example, central analysis system 406may determine that a first content item that received 300 requestsduring the last minute, 400 requests one to two minutes ago, and 600requests two to three minutes ago has a popularity value p₁ as shown inEquation 3:

$\begin{matrix}{p_{1} = {{\sum\limits_{j = 1}^{3}\frac{N_{j}}{j}} = {{\frac{300}{1} + \frac{400}{2} + \frac{600}{3}} = 700.}}} & (3)\end{matrix}$In another example, central analysis system 406 may determine that asecond content item that was streamed to 600 devices during the lastminute, 400 devices one to two minutes ago, and 300 devices two to threeminutes ago has a popularity value p₂ as shown in Equation 4:

$\begin{matrix}{p_{2} = {{\sum\limits_{j = 1}^{3}\frac{N_{j}}{j}} = {{\frac{600}{1} + \frac{400}{2} + \frac{300}{3}} = 900.}}} & (4)\end{matrix}$

Central analysis system 406 may determine a respective popularityranking value for each content item by comparing its respectivepopularity value to the respective popularity values of other contentitems in the content library. For example, central analysis system 406may determine a popularity ranking value r for each of the content itemsby sorting their respective popularity values from greatest to least. Tocontinue the aforementioned example, if the content library includesonly the first and second content items, central analysis system 406 maydetermine that the second content item has a popularity ranking valuer=1 and the first content item has a popularity ranking value r=2because the second content item's popularity value p₂ is greater thanthe first content item's popularity value p₁.

Central analysis system 406 may determine whether a content item ispopular, modestly popular, or unpopular based on popularity data 408.For example, central analysis system 406 may determine whether a contentitem is popular, modestly popular, or unpopular based on its popularityvalue, ranking value, or both. In some instances, central analysissystem 406 may perform the determination in real-time or near real-time(e.g., every 2 seconds) to accommodate rapid increases or decreases inthe number of the requests from devices 403 a-d.

Popular content items may correspond to content items with a popularityvalue above a first popularity threshold value or a popularity rankingabove a first popularity ranking threshold value. For example, centralanalysis system 406 may determine that content items with a popularityvalue p greater than or equal to a popularity threshold value of 100.00may be popular. In another example, central analysis system 406 maydetermine that content items with a popularity ranking value r less thanor equal to a popularity ranking threshold value of 14 (e.g., the 14most popular content items) may be popular.

Unpopular content items may correspond to content items with apopularity value below a second popularity threshold value or apopularity ranking below a second popularity ranking threshold value.For example, central analysis system 406 may determine that contentitems with a popularity value p greater less than or equal to apopularity threshold value of 2.00 may be unpopular. In another example,central analysis system 406 may determine that content items with apopularity ranking value r greater than or equal to a popularity rankingthreshold value of 30 (e.g., the 30th to the least popular contentitems) may be unpopular.

Modestly popular content items may correspond to content items with apopularity value between the first and second popularity thresholdvalues or a popularity ranking between the first and second popularityranking threshold values described above. For example, central analysissystem 406 may determine that content items with a popularity value pbetween a first popularity threshold value of 2.00 and a secondpopularity threshold value of 100.00 may be modestly popular. In anotherexample, central analysis system 406 may determine that content itemswith a popularity ranking value r between a first popularity rankingthreshold value of 14 and a second popularity ranking threshold value of30 (e.g., the 15th to 29th most popular content items) may be modestlypopular. In some embodiments, there may be more or fewer than threetiers of popularity (e.g., n-tiers, where n is an integer value), andcontent items may go through a smoothing function to prevent them frombouncing rapidly between tiers in time.

In some aspects, popularity may be a first-pass selection filter fordetermining where to store particular content items. For example, oncecentral analysis system 406 determines that a content item is a popularcontent item, it may filter a list of eligible hot caching devices basedon the ability of each hot caching device to serve the requested contentitem, the proximity between one or more devices and the hot cachingdevice, and the relative load of the hot caching device. For example,the threshold values discussed above may be dynamic threshold valuesbased on the number and sizes of the content items in the contentlibrary and the number and sizes of the caching devices in the CDN.Dynamic thresholds will be discussed further with reference to FIG. 7 .

Central analysis system 406 may move content items to differentpopularity tiers of caching devices in the popularity-based distributionhierarchy using any suitable technique, such as by transmittinginstructions to the caching devices via content router 404. For example,central analysis system 406 may move popular content items to hotcaching devices 402 a-c in tier 430, modestly popular content items tomedium caching devices 401 a-b in tier 420, and unpopular content itemsto cool caching device 400.

Central analysis system 406 may move a content item to a different tierof caching devices if the popularity of the content item changes whileit is being downloaded by one or more devices. For example, a contentitem may be served by a cool cache when a device initially begins todownload it. If central analysis system 406 determines that a movie hassuddenly become modestly popular or popular, it may move the movie to amedium caching device or hot caching device. Central analysis system 406may then signal the caching device to dynamically redirect the device torequest the movie from the medium or hot caching device for downloadingthe remainder of the movie. This redirection may go through the contentrouter, the caching device, or the device itself. This means that asudden increase in the popularity of a content item may, in someinstances, avoid overloading a cool caching device or create suddenlarge network load. Movement of a content item to a different tier ofcaching devices while it is being downloaded by a device will bediscussed further with reference to FIGS. 5A-5B and FIGS. 6A-6B.

Content router 404 may update content index 407 with new locationinformation for obtaining the original copies of the content items whenthey are moved to the different tiers of caching devices. Content router404 may access the updated content index 407 for use in redirectingdevices 403 a-d to the appropriate hot, medium, or cool caching devicewhere the original copy of the content item is stored. For example,content router 404 may receive a request for a content item from one ofdevices 403 a-d and access content index 407 to determine whether therequested content item is stored in a hot caching device, a mediumcaching device, or a cool caching device. If content router 404determines that the requested content item is stored in a tier of hotcaching devices, it may redirect the device to request the content itemfrom one of hot caching devices 402 a-c. If content router 404determines that the requested content item is stored in a tier of mediumcaching devices, it may redirect the device to request the content itemfrom one of medium caching devices 401 a-b. If content router 404determines that the requested content item is stored in a cool cachingdevice or a tier of cool caching devices, it may redirect the device torequest the content item from cool caching device 400.

In some embodiments, the path or location for accessing the content itemmay be determined dynamically, rather than being staticallypredetermined. For example, when a caching device (e.g., caching device402 a) becomes overloaded, it may remove itself from a list of eligiblecaching devices to serve a particular content item. Subsequently, a newcaching device (e.g., caching device 401 a) may be selected to servethat content item. When the overloaded caching device is no longeroverloaded, it may be placed on the list of eligible caching devicesagain and requests for that content item may again be redirected to theinitial caching device.

In one embodiment, the CDN may implement an initial popularity-baseddistribution hierarchy where all of the content items in the contentlibrary are stored in hot caching devices 402 a-c and assumed to beequally popular. For example, central analysis system 406 may assign, inpopularity data 408, a popularity value of 99999.99, a popularityranking of 1, or both to every content item in the content library. As aresult, content router 404 may initially route all requests for contentitems from devices 403 a-d to hot caching devices 402 a-c in tier 430.As time progresses and request history 405 grows larger, centralanalysis system 406 may determine that some of the initially popularcontent items may have become modestly popular or unpopular based ontheir respective popularity decreasing relative to other content items.Central analysis system 406 may move the modestly popular content itemsto medium caching devices 401 a-b in tier 420 and the unpopular contentitems to cool caching device 400 in tier 410. Content router 404 maythen redirect subsequent requests for the content items to the hot,medium, or cool caching device where the content item is stored.

In another embodiment, the CDN may implement an initial popularity-baseddistribution hierarchy where all of the content items in the contentlibrary are stored in cool caching device 400 and assumed to be equallyunpopular. For example, central analysis system 406 may assign, inpopularity data 408, a popularity value of 0.00, a popularity ranking of99999, or both to every content item in the content library. As aresult, content router 404 may initially route all requests for contentitems from devices 403 a-d to cool caching device 400 in tier 410. Astime progresses and request history 405 grows larger, central analysissystem 406 may determine that some of the initially unpopular contentitems may have become modestly popular or popular based on theirrespective popularity increasing relative to other content items.Central analysis system 406 may move the modestly popular content itemsto medium caching devices 401 a-b in tier 420 and the popular contentitems to hot caching devices 402 a-c in tier 430. Content router 404 maythen redirect subsequent requests for the content items to the hot,medium, or cool caching device where the content item is stored. In someembodiments, popularity values may also be initialized by other sources,such as box office popularity, viewership data for television shows,social media trending data, or any other suitable information from otherasset distribution services.

As illustrated in FIG. 4B, the popularity-based distribution hierarchymay allow device 403 to request and receive an unpopular content item,such as unpopular content item 450, directly from cool caching device400. Device 403 may request and receive a modestly popular content item,such as modestly popular content item 451, directly from medium cachingdevice 401. Device 403 may request and receive a popular content item,such as popular content item 452, directly from hot caching device 402.

In some aspects, the physical arrangement of the servers in thepopularity-based distribution hierarchy may differ from FIG. 4A. Asillustrated in FIG. 4C, each of the caching devices may be coupledthrough one or more communication networks 409 via a respective router408 a-1. One or more communication networks 409 may include thecommunication links 101 shown in FIG. 1 , one or more external networks109, one or more external networks 209 shown in FIG. 2 , or any othersuitable network or combination of networks. Routers 408 a-1 may containthe necessary routing tables and address information to transmitmessages to other devices coupled to one or more communication networks409, such as the content router, central analysis system, other cachingdevices and other devices in the CDN.

FIGS. 5A-5B illustrate an exemplary technique for moving a content item,such as content item 550, to a different tier of caching devices anddynamically redirecting a device to the different tier of cachingdevices if the popularity of the content item increases while it isbeing downloaded by the device. The technique illustrated in FIGS. 5A-5Bmay be implemented, for example, using the example popularity-baseddistribution hierarchy of FIGS. 4A-4C. The various components andprocesses described with reference to FIGS. 5A-5B may be implementedusing any suitable hardware, software, or both, such as device 200 shownin FIG. 2 . For purposes of illustration, popularity ranking value as afunction of time ƒ(t_(n))=r_(n) is represented as ƒ(t_(n))⁻¹=r_(n) ⁻¹such that the inverse of the popularity ranking value increases as thepopularity of content item 550 increases over time (i.e., as thenumerical value of the popularity ranking value r decreases).

As illustrated in FIG. 5A, content item 550 may have an increasingpopularity ranking value r⁻¹ over time as indicated by popularityranking curve 510. The central analysis system may determine popularityranking value r⁻¹ as a function of time using any suitable technique,such as by comparing its respective popularity value p to the respectivepopularity values of other content items in the content library. Forexample, the central analysis system may determine that content item 550has a popularity ranking value 511 at time 501, a popularity rankingvalue 512 at time 502, and a popularity ranking value 513 at time 503.The central analysis system may also store popularity ranking thresholdvalue 520 and popularity ranking threshold value 530. One or both ofpopularity ranking threshold values 520 and 530 may be dynamic thresholdvalues determined by the central analysis system based on, for example,the number and sizes of the content items in the content library and thenumber and sizes of the caching devices in the CDN.

In an illustrative example, content item 550 may initially be anunpopular content item stored in cool caching device 400. At time 501,the central analysis system may determine that content item 550 has apopularity corresponding to popularity ranking value 511. At time 502,the central analysis system may determine that content item 550 has apopularity corresponding to popularity ranking value 512, which may be apopularity ranking value r⁻¹ equal to or greater than popularity rankingthreshold value 520. As a result, the central analysis system maydetermine that content item 550 is a modestly popular content item andmove content item 550 from cool caching device 400 to medium cachingdevice 401. Subsequently, at time 503, the central analysis system maydetermine that content item 550 has a popularity corresponding topopularity ranking value 513, which may be a popularity ranking valuer⁻¹ equal to or greater than popularity ranking threshold value 530. Asa result, the central analysis system may determine that content item550 is a popular content item and move content item 550 from mediumcaching device 401 to hot caching device 402.

As illustrated in FIG. 5B, the central analysis system may signal thecaches or clients to dynamically redirect a request for content item 550or portions thereof to one or more cool caching devices 400, one or moremedium caching devices 401, or one or more hot caching devices 402 basedon where content item 550 is stored at a particular point in time. Forexample, at time 501, device 403 may request and receive a first portionof content item 550 directly from cool caching device 400. The firstportion may correspond to, for example, the first five segments (e.g.,the first 10 seconds) of a movie that has been segmented into two-secondvideo fragments. At time 502, the content router may redirect thedevice's request for a second portion of content item 550 to mediumcaching device 401. The second portion may correspond to, for example,the next two segments (e.g., the next 11-14 seconds) of the movie. As aresult, device 403 may request and receive the second portion of contentitem 550 directly from medium caching device 401. At time 503, thecontent router may redirect the device's request for a third portion ofcontent item 550 to hot caching device 402. The third portion maycorrespond to, for example, the remaining segments (e.g., seconds 15onward) of the movie until the entire movie is received by device 403 oruntil device 403 is redirected to request a subsequent portion of themovie from a medium or cool caching device. As a result, device 403 mayrequest and receive the third portion of content item 550 directly fromhot caching device 402.

FIGS. 6A-6B illustrate an exemplary technique for moving a content item,such as content item 650, to a different tier of caching devices anddynamically redirecting a device to the different tier of cachingdevices if the popularity of the content item decreases while it isbeing downloaded by the device.

As illustrated in FIG. 6A, content item 650 may have a decreasingpopularity ranking value ƒ(t_(n))=r_(n) over time as indicated bypopularity ranking curve 610, where ƒ(t_(n))=r_(n) is represented asƒ(t_(n))⁻¹=r_(n) ⁻¹ for purposes of illustration. For example, thecentral analysis system may determine popularity ranking value 611 attime 601, popularity ranking value 612 at time 602, and popularityranking value 613 at time 603. The central analysis system may alsostore popularity ranking threshold value 620 and popularity rankingthreshold value 630. One or both of popularity ranking threshold values620 and 630 may be dynamic threshold values determined by the centralanalysis system based on, for example, the number and sizes of thecontent items in the content library and the number and sizes of thecaching devices in the CDN.

In an illustrative example, content item 650 may initially be a popularcontent item stored in hot caching device 402. At time 601, the centralanalysis system may determine that content item 650 has a popularitycorresponding to popularity ranking value 611. At time 602, the centralanalysis system may determine that content item 650 has a popularitycorresponding to popularity ranking value 612, which may be a popularityranking value r⁻¹ equal to or less than popularity ranking thresholdvalue 630. As a result, the central analysis system may determine thatcontent item 650 is a modestly popular content item and move contentitem 650 from hot caching device 402 to medium caching device 401.Subsequently, at time 603, the central analysis system may determinethat content item 650 has a popularity corresponding to popularityranking value 613, which may be a popularity ranking value r⁻¹ equal toor less than popularity ranking threshold value 620. As a result, thecentral analysis system may determine that content item 650 is anunpopular content item and move content item 650 from medium cachingdevice 401 to cool caching device 400.

As illustrated in FIG. 6B, the content router may dynamically redirect arequest for content item 650 or portions thereof to cool caching device400, medium caching device 401, or hot caching device 402 based on wherecontent item 650 is stored at a particular point in time. For example,at time 601, device 403 may request and receive a first portion ofcontent item 650 directly from hot caching device 402. At time 602, thecontent router may redirect the device's request for a second portion ofcontent item 650 to medium caching device 401. As a result, device 403may request and receive the second portion of content item 650 directlyfrom medium caching device 401. At time 603, the content router mayredirect the device's request for a third portion of content item 650 tocool caching device 400. As a result, device 403 may request and receivethe third portion of content item 650 directly from cool caching device400.

FIG. 7 illustrates an exemplary technique for determining a dynamicthreshold value. For example, the popularity threshold values,popularity ranking threshold values, or both may be configurable. Graph700 shows a distribution of requests (or streams) for the content itemsin the CDN's content library. As illustrated in FIG. 7 , the majority ofrequests are for content items with a popularity ranking value equal toor less than a first popularity ranking threshold value 770 of 14.Therefore, it may be efficient to serve content items 701 to 714 (e.g.,the 14 most popular content items) using one or more hot cachingdevices. It may be efficient to serve content items 715 to 729, whichcorrespond to content items with a popularity ranking value greater thanthe first popularity ranking threshold value 770 of 14 but less than orequal to a second popularity ranking value 780 of 29, using one or moremedium caching devices. It may be efficient to serve content items 730and the remainder of the content items, which correspond to contentitems with a popularity ranking value greater than the second popularityranking value 780 of 29, using a cool caching device.

Graph 700 may change as the size of the CDN's content library grows andthe respective popularity ranking values of the content items change. Asthe information depicted by graph 700 changes, the central analysissystem may change one or both of popularity ranking threshold values 770and 780. For example, popularity ranking threshold values 770 and 780may be dynamic threshold values based on the number and sizes of thecontent items in the content library and the number and sizes of thecaching devices in the CDN. In an example, if the average size of thecontent items is X and the size of hot caching device size is Y, thenumber of content items that may be stored in the hot caching device maybe Z=Y/X. The central analysis system may determine that content itemswith a popularity ranking value less than or equal to a popularityranking threshold value 770 of Z (e.g., the Z most popular contentitems) may be popular content items and store them in the hot cachingdevice. Dynamic threshold values may provide the central analysis systemor its operator with the ability to tune the CDN for events such asrequests storms.

FIG. 8 illustrates an example process flow for providing a content itemto a device using a popularity-based distribution hierarchy, such as thepopularity-based distribution hierarchy described with reference toFIGS. 4A-4C.

At step 801, the central analysis system determines the popularity of acontent item, such as a media content item or other data, stored in afirst caching device. The first caching device may be, for example, oneof hot caching device 402, medium caching device 401, or cool cachingdevice 400 described with reference to FIGS. 4A-4C, 5A-5B, and 6A-6B.

At step 802, the central analysis system determines whether thepopularity of the content item is beyond a threshold value. If thecentral analysis system determines that a change in the popularity ofthe content item is above a threshold value, the process may proceed tostep 803. If the central analysis system determines that the popularityof the content item is not beyond a threshold value, the process may endand the content item may remain stored in the first caching device.

At step 803, the central analysis system moves the content item to asecond caching device in a different popularity tier and deletes it fromits original source. At step 804, the central analysis system may causea device, such as a user device, to be redirected to request the contentitem from the second caching device.

The various features described above are merely nonlimiting examples,and can be rearranged, combined, subdivided, omitted, and/or altered inany desired manner. For example, features of the servers can besubdivided among multiple processors and computing devices. The truescope of this patent should only be defined by the claims that follow.

The invention claimed is:
 1. A method comprising: causing, by acomputing device, a first caching device to store a first portion of acontent item, wherein the first caching device is in a first tier of ahierarchy of caching devices; determining, by the computing device: afirst weighted popularity, associated with a first time, of a secondportion of the content item, and a second weighted popularity,associated with a second time, of the second portion of the contentitem, wherein the first weighted popularity is weighted differently fromthe second weighted popularity; causing, by the computing device andbased on the first weighted popularity and the second weightedpopularity, a second caching device to store the second portion of thecontent item, wherein the second caching device is in a second tier ofthe hierarchy of caching devices, and wherein the second tier isdifferent from the first tier; receiving, by the computing device, arequest for the content item; determining that the first caching deviceand the second caching device each store at least a portion of thecontent item; causing, by the computing device and based on the requestfor the content item, the first portion of the content item to berequested from the first caching device; and causing, by the computingdevice and based on the request for the content item, the second portionof the content item to be requested from the second caching device. 2.The method of claim 1, wherein the second portion is stored in thesecond caching device prior to causing a user device to request thefirst portion.
 3. The method of claim 1, wherein the first cachingdevice does not store the second portion when the second caching devicestores the second portion, and wherein the second caching device doesnot store the first portion when the first caching device stores thefirst portion.
 4. The method of claim 1, further comprising, updating,after causing the second caching device to store the second portion ofthe content item, a content index to indicate that the second portionhas been stored in the second caching device.
 5. The method of claim 1,wherein the first time is earlier than the second time, and wherein thefirst time is weighted less than the second time.
 6. The method of claim5, wherein the causing the second caching device to store the secondportion of the content item is further based on a popularity valuecalculated from the first weighted popularity and the second weightedpopularity satisfying a threshold.
 7. The method of claim 1, wherein thecontent item comprises a video, wherein the first portion of the contentitem comprises a first portion of the video, and wherein the secondportion of the content item comprises a second portion of the videodifferent from the first portion.
 8. An apparatus comprising: one ormore processors; and first memory storing instructions that, whenexecuted by the one or more processors, cause the apparatus to: store afirst portion of a content item in a first caching device, wherein thefirst caching device is in a first tier of a hierarchy of cachingdevices; determine: a first weighted popularity, associated with a firsttime, of a second portion of the content item, and a second weightedpopularity, associated with a second time, of the second portion of thecontent item, wherein the first weighted popularity is weighteddifferently from the second weighted popularity; store, based on thefirst weighted popularity and the second weighted popularity, the secondportion of the content item in a second caching device, wherein thesecond caching device is in a second tier of the hierarchy of cachingdevices, and wherein the second tier is different from the first tier;receive a request for the content item; determine that the first cachingdevice and the second caching device each store at least a portion ofthe content item; initiate, based on the request for the content item, afirst request for the first portion of the content item from the firstcaching device; and initiate, based on the request for the content item,a second request for the second portion of the content item from thesecond caching device.
 9. The apparatus of claim 8, wherein the secondportion is stored in the second caching device prior to causing a userdevice to request the first portion.
 10. The apparatus of claim 8,wherein the first caching device does not store the second portion whenthe second caching device stores the second portion, and wherein thesecond caching device does not store the first portion when the firstcaching device stores the first portion.
 11. The apparatus of claim 8,wherein the instructions, when executed by the one or more processors,cause the apparatus to update, after storing the second portion of thecontent item, a content index to indicate that the second portion hasbeen stored in the second caching device.
 12. The apparatus of claim 8,wherein each of the caching devices stores a plurality of content items,and wherein the instructions, when executed by the one or moreprocessors, cause the apparatus to: receive, from each of the cachingdevices, respective popularity data for each of the plurality of contentitems.
 13. The apparatus of claim 8, wherein the first time is earlierthan the second time, and wherein the first time is weighted less thanthe second time.
 14. The apparatus of claim 13, wherein the storing thesecond portion of the content item is further based on a popularityvalue calculated from the first weighted popularity and the secondweighted popularity satisfying a threshold.
 15. At least onenon-transitory computer readable storage medium storing computerreadable instructions which, when executed, cause: storage of a firstportion of a content item in a first caching device, wherein the firstcaching device is in a first tier of a hierarchy of caching devices;determining: a first weighted popularity, associated with a first time,of a second portion of the content item, and a second weightedpopularity, associated with a second time, of the second portion of thecontent item, wherein the first weighted popularity is weighteddifferently from the second weighted popularity; storage, based on thefirst weighted popularity and the second weighted popularity, of thesecond portion of the content item in a second caching device, whereinthe second caching device is in a second tier of the hierarchy ofcaching devices, and wherein the second tier is different from the firsttier; receiving a request for the content item; determining that thefirst caching device and the second caching device each store at least aportion of the content item; requesting, from the first caching deviceand based on the request for the content item, the first portion of thecontent item; and requesting, from the second caching device and basedon the request for the content item, the second portion of the contentitem.
 16. The at least one non-transitory computer readable storagemedium of claim 15, wherein the second portion is stored in the secondcaching device prior to requesting the first portion.
 17. The at leastone non-transitory computer readable storage medium of claim 15, whereinthe first caching device does not store the second portion when thesecond caching device stores the second portion, and wherein the secondcaching device does not store the first portion when the first cachingdevice stores the first portion.
 18. The at least one non-transitorycomputer readable storage medium of claim 15, wherein the computerreadable instructions, when executed, cause updating, after storage ofthe second portion of the content item, a content index to indicate thatthe second portion has been stored in the second caching device.
 19. Theat least one non-transitory computer readable storage medium of claim15, wherein each of the caching devices stores a plurality of contentitems, and wherein the computer readable instructions, when executed,cause: receiving, from each of the caching devices, respectivepopularity data for each of the plurality of content items.
 20. The atleast one non-transitory computer readable storage medium of claim 15,wherein the first time is earlier than the second time, wherein thefirst time is weighted less than the second time, and wherein thestorage of the second portion of the content item is further based on apopularity value calculated from the first weighted popularity and thesecond weighted popularity satisfying a threshold.